Jab We Met is very enjoyable. After a long time, we have a fabulous romantic comedy.
The plot is not original, but the script is well written. A big part of this movie is on Karina's shoulders and she carries it really well. She rocks! This is probably her best performance so far.
Two songs are very melodious and with nice poetry: Aaoge Tum (Music: Sandesh Shandilya, Lyrics: Nida Fazli?, Singer: Ustad Rashid Khan) and Tum Se Hi (Music: Pritam, Lyrics: Irshad Kamil, Singer: Mohit Chauhan). They are well placed in the movie. The placement of other songs could have been better though. They don’t move the story forward but don’t do much damage to the flow either.
Some moments in the movie were really touching and well executed: When Aditya accepts that he’s in love with Geet and says “Yes, I like you a lot, but hey, that’s my problem!”, when during their second stay in a (this time literally decent!) motel she says something like "Kitni stupid hoon main, is liye meri ye haalat hui hai" and starts crying, when Geet finally realizes what her heart wants, a train passes in the background and she feels "jaise koi train chhut rahi ho…”
Imtiaz Ali has grown as a director after his first movie Socha Na Tha (which was quite likeable). The opening sequence: when Aditya leaves his cell phone and car keys and walks into the streets dropping his tie on the sidewalk, was very impressive.
I am looking forward to Imtiaz Ali’s next movie.
--------------
Also, I highly recommend Manorama Six Feet Under and Johny Gaddar to everyone who likes off-beat movies. Both are very well-made and well-acted movies in genres that are almost absent in Bollywood nowadays. The former is a Bollywood noir set up in a remote village in Rajasthan (spectacular visuals). And the latter is a suspense thriller, in which the audience knows who the culprit is, but the actors in the movie don't, and the movie is about how they find it out! Look out for pop-culture references to the movies from 70's. There are so many.
Unfortunately, No Smoking, on which I had high hopes (especially because of the names that are associated with the movie - Vishal Bhardwaj and Gulzar), didn't get any good reviews. It looks like Anurag Kashyap has broken bridges with so many reviewers that many of them are reviewing his attitude rather than his movie. (Read Khalid Mohammed's review in Hindustan Times, you don't need an expert to figure out that he has personal grudges for Kashyap and that's all he talks about in his "movie" review - bad director, bad person, very arrogant, how stupid yada yada yada...) I still haven't given up on the movie though -- I will watch it as soon as I can.
Saturday, October 27, 2007
Wednesday, October 24, 2007
Yahoo Driving Directions in India
.
You can go to Yahoo website to get driving directions between two cities in India. It seems a tad slow, it's definetely not perfect (works well between two cities only), but it's a great start.
Here's the link: http://in.maps.yahoo.com/
Note that directions include landscapes (turn left past Bharat Petrolium, turn right past Andhra bank etc.), which are unavoidable because we don't have names for all streets, do we?
Enjoy!
You can go to Yahoo website to get driving directions between two cities in India. It seems a tad slow, it's definetely not perfect (works well between two cities only), but it's a great start.
Here's the link: http://in.maps.yahoo.com/
Note that directions include landscapes (turn left past Bharat Petrolium, turn right past Andhra bank etc.), which are unavoidable because we don't have names for all streets, do we?
Enjoy!
Tuesday, October 23, 2007
Giant Douche and Turd Sandwich
.
Although I am not a conservative, as a libertarian my favourite presidential candidate is the Republican representative from Texas: Ron Paul.
I definitely don't agree with all of his plans and policies (that I know of). Also he might be too old and doesn't look like the "strong leader" that US is looking forward to have as a President, but I think Paul is the candidate whom I dislike the least. As one of the South Park episode has succinctly put: you have to make a choice between a Giant Douche and a Turn Sandwich!
Here's a snapshot of some of his beliefs and actions:
- He opposes, and have always opposed, Iraq war.
- He wants USA to be out of UN and other such international groups.
- He believes in: Abolishing Federal taxes, dismantle Federal reserve and get back to god-backed currency.
- He supports Gun Ownership.
- He says no to US interventions in other country's internal affairs.
Here's a link to his web-site: http://www.ronpaul2008.com/
Giuliani, Romney and McCain are currently the forerunners at Republican presidential nominee race, but it looks like none of these candidates can win an election against Clinton. In fact, any Republican presidential candidates seems very likely loose election against Clinton.
Senator Clinton, who is considered a socialist by many people now, wants the Government to "take over" Health Care system. It looks like by increasing taxes, she would go on a spending spree if elected as a President. She must be stopped! But unfortunately if looks like no one will be able to do that.
I am not a US citizen, so I don't vote here, but being in this country for several years, I have my emotional investments in next year's presidential election.
Although I am not a conservative, as a libertarian my favourite presidential candidate is the Republican representative from Texas: Ron Paul.
I definitely don't agree with all of his plans and policies (that I know of). Also he might be too old and doesn't look like the "strong leader" that US is looking forward to have as a President, but I think Paul is the candidate whom I dislike the least. As one of the South Park episode has succinctly put: you have to make a choice between a Giant Douche and a Turn Sandwich!
Here's a snapshot of some of his beliefs and actions:
- He opposes, and have always opposed, Iraq war.
- He wants USA to be out of UN and other such international groups.
- He believes in: Abolishing Federal taxes, dismantle Federal reserve and get back to god-backed currency.
- He supports Gun Ownership.
- He says no to US interventions in other country's internal affairs.
Here's a link to his web-site: http://www.ronpaul2008.com/
Giuliani, Romney and McCain are currently the forerunners at Republican presidential nominee race, but it looks like none of these candidates can win an election against Clinton. In fact, any Republican presidential candidates seems very likely loose election against Clinton.
Senator Clinton, who is considered a socialist by many people now, wants the Government to "take over" Health Care system. It looks like by increasing taxes, she would go on a spending spree if elected as a President. She must be stopped! But unfortunately if looks like no one will be able to do that.
I am not a US citizen, so I don't vote here, but being in this country for several years, I have my emotional investments in next year's presidential election.
Friday, October 19, 2007
My job at MTC
>
I am currently working as a Business Analyst at a major telecom company (let’s call it MTC). This “Business Analyst” title can be expanded as Statistician/SAS Programmer/Predictive Modeler and can be expressed as “Data Miner” in general. There are about 15 members (including one director and 4 managers) in our group named Customer Analytics. At MTC, we are known as "Modelers", and belong to the Marketing branch of the organization hierarchy.
In a nutshell, we do data mining, statistical modeling and clustering & segmentation which enables us to make business predictions that can help the marketing group to take business decisions.
Let me briefly talk about the need for Business Analytics in a telecom company.When a telecom company acquires a new customer, they have to spend money. This spending includes everything they have to do for getting a new customer: advertising, giving subsidies on handsets, additional network capacity to accommodate the new customer calls, more care center representatives needed to handle calls to the company etc. It takes months before MTC can recover from the acquisition spending for each new customer and start making money from that account. If a customer leaves the company after, let’s say two months, the company actually loses money. (Even the early contract termination fees can not balance the loss in revenue.)
Moreover, it's cheaper and easier to retain an old customer than to acquire a new customer.
Thus, while trying to attract new customers, telecom companies are always more concerned with retaining the existing customer base.
Here’s where the role of our group comes into the picture. MTC needs to know which customers are 'more likely to leave', and when they are likely to do so. If we knew that, then the Marketing group can take necessary actions to retain those customers. To find out whom to contact, and when to contact them they depend on us. (On our predictions based on statistical models, to be more specific.)
Our job is to study the customer history (cell usage, call details, demographics, late payments, purchase patterns etc.) and look for behavioral patterns that can explain what triggered previous MTC customers to leave. Based on the historical data, we build Statistical (predictive) models using SAS. These models can be applied to the current customer base (whose behavior is known to us) to decide which customers are likely to leave (in, let’s say, next two or three months).
After the very interesting phase of data discovery, modeling and prediction, comes more challenging task. We have to present our model to the Marketing managers (decision makers) and explain them what’s going on with our customers. Obviously, we have to present our findings in such a way that they are easier to digest for a non-technical marketing folks. The marketers will take this result and run various campaigns (offers, incentives) to retain those customers. In doing so, they will be spending millions of dollars, so our results must be: (a) as accurate as possible, (b) meaningful, and (c) actionable.
This Churn/Risk Modeling is one among the many other things we do. Here are some scenarios in which the seek help from our group:
− The company launches new products and they need to know which customers they should target. So we will build a model that predicts which customers are more likely to respond to that specific product related offer. (MTC has millions of customers, so they can’t go and target all customers. We need to find out which customers will be most interested and target them to increase our ROI.)
− The company wants to identify which customers are more valuable (profitable). To get the holistic view of a customer; in addition to look at “what’s the customer worth as of today” it’s necessary to look into the future and see “what’s the potential value of the customer” too. Using various statistical modeling techniques, we can predict (a) future tenure, and (b) future revenue stream of a customer. (Interestingly, when you call the customer service, your “value” decides which call center will handle your call. A call from a low value customer might get transferred to international call centers, while a high value customer will get to talk with a US based call center. Also, if you decide to terminate your contract, your “value” can heavily restrict how much you can negotiate on contract termination fees.)
The use of data mining and statistical modeling is, of course, not limited to telecom industry. Think about an insurance company that wants to identify which customers are likely to be frauds, a bank or credit company that needs to know who is likely to close the account or which transactions are likely to be fraudulent, a pharmaceutical company that wants to identify which drug component is likely to affect as a remedy on a decease while developing a new drug, a giant retailer that needs recommendations about which products they should keep together in the store (which products do customer buy together?), or an online retailer (like Amazon) that gets help from data mining to come up with recommendations for next purchase.
I am currently working as a Business Analyst at a major telecom company (let’s call it MTC). This “Business Analyst” title can be expanded as Statistician/SAS Programmer/Predictive Modeler and can be expressed as “Data Miner” in general. There are about 15 members (including one director and 4 managers) in our group named Customer Analytics. At MTC, we are known as "Modelers", and belong to the Marketing branch of the organization hierarchy.
In a nutshell, we do data mining, statistical modeling and clustering & segmentation which enables us to make business predictions that can help the marketing group to take business decisions.
Let me briefly talk about the need for Business Analytics in a telecom company.When a telecom company acquires a new customer, they have to spend money. This spending includes everything they have to do for getting a new customer: advertising, giving subsidies on handsets, additional network capacity to accommodate the new customer calls, more care center representatives needed to handle calls to the company etc. It takes months before MTC can recover from the acquisition spending for each new customer and start making money from that account. If a customer leaves the company after, let’s say two months, the company actually loses money. (Even the early contract termination fees can not balance the loss in revenue.)
Moreover, it's cheaper and easier to retain an old customer than to acquire a new customer.
Thus, while trying to attract new customers, telecom companies are always more concerned with retaining the existing customer base.
Here’s where the role of our group comes into the picture. MTC needs to know which customers are 'more likely to leave', and when they are likely to do so. If we knew that, then the Marketing group can take necessary actions to retain those customers. To find out whom to contact, and when to contact them they depend on us. (On our predictions based on statistical models, to be more specific.)
Our job is to study the customer history (cell usage, call details, demographics, late payments, purchase patterns etc.) and look for behavioral patterns that can explain what triggered previous MTC customers to leave. Based on the historical data, we build Statistical (predictive) models using SAS. These models can be applied to the current customer base (whose behavior is known to us) to decide which customers are likely to leave (in, let’s say, next two or three months).
After the very interesting phase of data discovery, modeling and prediction, comes more challenging task. We have to present our model to the Marketing managers (decision makers) and explain them what’s going on with our customers. Obviously, we have to present our findings in such a way that they are easier to digest for a non-technical marketing folks. The marketers will take this result and run various campaigns (offers, incentives) to retain those customers. In doing so, they will be spending millions of dollars, so our results must be: (a) as accurate as possible, (b) meaningful, and (c) actionable.
This Churn/Risk Modeling is one among the many other things we do. Here are some scenarios in which the seek help from our group:
− The company launches new products and they need to know which customers they should target. So we will build a model that predicts which customers are more likely to respond to that specific product related offer. (MTC has millions of customers, so they can’t go and target all customers. We need to find out which customers will be most interested and target them to increase our ROI.)
− The company wants to identify which customers are more valuable (profitable). To get the holistic view of a customer; in addition to look at “what’s the customer worth as of today” it’s necessary to look into the future and see “what’s the potential value of the customer” too. Using various statistical modeling techniques, we can predict (a) future tenure, and (b) future revenue stream of a customer. (Interestingly, when you call the customer service, your “value” decides which call center will handle your call. A call from a low value customer might get transferred to international call centers, while a high value customer will get to talk with a US based call center. Also, if you decide to terminate your contract, your “value” can heavily restrict how much you can negotiate on contract termination fees.)
The use of data mining and statistical modeling is, of course, not limited to telecom industry. Think about an insurance company that wants to identify which customers are likely to be frauds, a bank or credit company that needs to know who is likely to close the account or which transactions are likely to be fraudulent, a pharmaceutical company that wants to identify which drug component is likely to affect as a remedy on a decease while developing a new drug, a giant retailer that needs recommendations about which products they should keep together in the store (which products do customer buy together?), or an online retailer (like Amazon) that gets help from data mining to come up with recommendations for next purchase.
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